How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models

• URL: http://arxiv.org/abs/2209.01390v1
• Date: Sat, 3 Sep 2022 10:16:34 GMT
• Title: How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models
• Authors: Hai Dang, Lukas Mecke, Florian Lehmann, Sven Goller, Daniel Buschek
• Abstract summary: We discuss the opportunities and challenges for interactive creative applications that use prompting as a new paradigm for Human-AI interaction. Based on our analysis, we propose four design goals for user interfaces that support prompting. We illustrate these with concrete UI design sketches, focusing on the use case of creative writing.
• Score: 29.420160518026496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep generative models have the potential to fundamentally change the way we create high-fidelity digital content but are often hard to control. Prompting a generative model is a promising recent development that in principle enables end-users to creatively leverage zero-shot and few-shot learning to assign new tasks to an AI ad-hoc, simply by writing them down. However, for the majority of end-users writing effective prompts is currently largely a trial and error process. To address this, we discuss the key opportunities and challenges for interactive creative applications that use prompting as a new paradigm for Human-AI interaction. Based on our analysis, we propose four design goals for user interfaces that support prompting. We illustrate these with concrete UI design sketches, focusing on the use case of creative writing. The research community in HCI and AI can take these as starting points to develop adequate user interfaces for models capable of zero- and few-shot learning.

Related papers

• Can AI Be as Creative as Humans? [84.43873277557852]
  We prove in theory that AI can be as creative as humans under the condition that it can properly fit the data generated by human creators. The debate on AI's creativity is reduced into the question of its ability to fit a sufficient amount of data.
  arXiv  Detail & Related papers  (2024-01-03T08:49:12Z)
• The role of interface design on prompt-mediated creativity in Generative AI [0.0]
  We analyze more than 145,000 prompts from two Generative AI platforms. We find that users exhibit a tendency towards exploration of new topics over exploitation of concepts visited previously.
  arXiv  Detail & Related papers  (2023-11-30T22:33:34Z)
• Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation [19.62178304006683]
  We argue that current interaction paradigms fall short, guiding users towards rapid convergence on a limited set of ideas. We propose a framework that facilitates the structured generation of design space in which users can seamlessly explore, evaluate, and synthesize a multitude of responses.
  arXiv  Detail & Related papers  (2023-10-19T17:53:14Z)
• The Creative Frontier of Generative AI: Managing the Novelty-Usefulness Tradeoff [0.4873362301533825]
  We explore the optimal balance between novelty and usefulness in generative Artificial Intelligence (AI) systems. Overemphasizing either aspect can lead to limitations such as hallucinations and memorization.
  arXiv  Detail & Related papers  (2023-06-06T11:44:57Z)
• Interactive Text Generation [75.23894005664533]
  We introduce a new Interactive Text Generation task that allows training generation models interactively without the costs of involving real users. We train our interactive models using Imitation Learning, and our experiments against competitive non-interactive generation models show that models trained interactively are superior to their non-interactive counterparts.
  arXiv  Detail & Related papers  (2023-03-02T01:57:17Z)
• Opportunities and Challenges in Neural Dialog Tutoring [54.07241332881601]
  We rigorously analyze various generative language models on two dialog tutoring datasets for language learning. We find that although current approaches can model tutoring in constrained learning scenarios, they perform poorly in less constrained scenarios. Our human quality evaluation shows that both models and ground-truth annotations exhibit low performance in terms of equitable tutoring.
  arXiv  Detail & Related papers  (2023-01-24T11:00:17Z)
• Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models [116.25562358482962]
  State-of-the-art neural language models can be used to solve ad-hoc language tasks without the need for supervised training. PromptIDE allows users to experiment with prompt variations, visualize prompt performance, and iteratively optimize prompts.
  arXiv  Detail & Related papers  (2022-08-16T17:17:53Z)
• Creative Wand: A System to Study Effects of Communications in Co-Creative Settings [9.356870107137093]
  Co-creative, mixed-initiative systems require user-centric means of influencing the algorithm. Key questions in co-creative AI include: How can users express their creative intentions? We introduce CREATIVE-WAND, a customizable framework for investigating co-creative mixed-initiative generation.
  arXiv  Detail & Related papers  (2022-08-04T20:56:40Z)
• Few-shot Prompting Towards Controllable Response Generation [49.479958672988566]
  We first explored the combination of prompting and reinforcement learning (RL) to steer models' generation without accessing any of the models' parameters. We apply multi-task learning to make the model learn to generalize to new tasks better. Experiment results show that our proposed method can successfully control several state-of-the-art (SOTA) dialogue models without accessing their parameters.
  arXiv  Detail & Related papers  (2022-06-08T14:48:06Z)
• WenLan 2.0: Make AI Imagine via a Multimodal Foundation Model [74.4875156387271]
  We develop a novel foundation model pre-trained with huge multimodal (visual and textual) data. We show that state-of-the-art results can be obtained on a wide range of downstream tasks.
  arXiv  Detail & Related papers  (2021-10-27T12:25:21Z)
• Human in the Loop for Machine Creativity [0.0]
  We conceptualize existing and future human-in-the-loop (HITL) approaches for creative applications. We examine and speculate on long term implications for models, interfaces, and machine creativity. We envision multimodal HITL processes, where texts, visuals, sounds, and other information are coupled together, with automated analysis of humans and environments.
  arXiv  Detail & Related papers  (2021-10-07T15:42:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.